{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7d97442d",
   "metadata": {
    "origin_pos": 0
   },
   "source": [
    "# 查阅文档\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07a073d4",
   "metadata": {
    "origin_pos": 2,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "由于篇幅限制，本书不可能介绍每一个PyTorch函数和类。\n",
    "API文档、其他教程和示例提供了本书之外的大量文档。\n",
    "本节提供了一些查看PyTorch API的指导。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5b25fc6",
   "metadata": {
    "origin_pos": 4
   },
   "source": [
    "## 查找模块中的所有函数和类\n",
    "\n",
    "为了知道模块中可以调用哪些函数和类，可以调用`dir`函数。\n",
    "例如，我们可以(**查询随机数生成模块中的所有属性：**)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5639656b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T16:28:23.452449Z",
     "iopub.status.busy": "2022-12-07T16:28:23.451915Z",
     "iopub.status.idle": "2022-12-07T16:28:24.981263Z",
     "shell.execute_reply": "2022-12-07T16:28:24.980388Z"
    },
    "origin_pos": 6,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['AbsTransform', 'AffineTransform', 'Bernoulli', 'Beta', 'Binomial', 'CatTransform', 'Categorical', 'Cauchy', 'Chi2', 'ComposeTransform', 'ContinuousBernoulli', 'CorrCholeskyTransform', 'CumulativeDistributionTransform', 'Dirichlet', 'Distribution', 'ExpTransform', 'Exponential', 'ExponentialFamily', 'FisherSnedecor', 'Gamma', 'Geometric', 'Gumbel', 'HalfCauchy', 'HalfNormal', 'Independent', 'IndependentTransform', 'Kumaraswamy', 'LKJCholesky', 'Laplace', 'LogNormal', 'LogisticNormal', 'LowRankMultivariateNormal', 'LowerCholeskyTransform', 'MixtureSameFamily', 'Multinomial', 'MultivariateNormal', 'NegativeBinomial', 'Normal', 'OneHotCategorical', 'OneHotCategoricalStraightThrough', 'Pareto', 'Poisson', 'PowerTransform', 'RelaxedBernoulli', 'RelaxedOneHotCategorical', 'ReshapeTransform', 'SigmoidTransform', 'SoftmaxTransform', 'SoftplusTransform', 'StackTransform', 'StickBreakingTransform', 'StudentT', 'TanhTransform', 'Transform', 'TransformedDistribution', 'Uniform', 'VonMises', 'Weibull', 'Wishart', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 'bernoulli', 'beta', 'biject_to', 'binomial', 'categorical', 'cauchy', 'chi2', 'constraint_registry', 'constraints', 'continuous_bernoulli', 'dirichlet', 'distribution', 'exp_family', 'exponential', 'fishersnedecor', 'gamma', 'geometric', 'gumbel', 'half_cauchy', 'half_normal', 'identity_transform', 'independent', 'kl', 'kl_divergence', 'kumaraswamy', 'laplace', 'lkj_cholesky', 'log_normal', 'logistic_normal', 'lowrank_multivariate_normal', 'mixture_same_family', 'multinomial', 'multivariate_normal', 'negative_binomial', 'normal', 'one_hot_categorical', 'pareto', 'poisson', 'register_kl', 'relaxed_bernoulli', 'relaxed_categorical', 'studentT', 'transform_to', 'transformed_distribution', 'transforms', 'uniform', 'utils', 'von_mises', 'weibull', 'wishart']\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "print(dir(torch.distributions))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c95a230",
   "metadata": {
    "origin_pos": 9
   },
   "source": [
    "通常可以忽略以“`__`”（双下划线）开始和结束的函数，它们是Python中的特殊对象，\n",
    "或以单个“`_`”（单下划线）开始的函数，它们通常是内部函数。\n",
    "根据剩余的函数名或属性名，我们可能会猜测这个模块提供了各种生成随机数的方法，\n",
    "包括从均匀分布（`uniform`）、正态分布（`normal`）和多项分布（`multinomial`）中采样。\n",
    "\n",
    "## 查找特定函数和类的用法\n",
    "\n",
    "有关如何使用给定函数或类的更具体说明，可以调用`help`函数。\n",
    "例如，我们来[**查看张量`ones`函数的用法。**]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "701b67a7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T16:28:24.985057Z",
     "iopub.status.busy": "2022-12-07T16:28:24.984521Z",
     "iopub.status.idle": "2022-12-07T16:28:24.990020Z",
     "shell.execute_reply": "2022-12-07T16:28:24.989276Z"
    },
    "origin_pos": 11,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function ones in module torch:\n",
      "\n",
      "ones(...)\n",
      "    ones(*size, *, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) -> Tensor\n",
      "    \n",
      "    Returns a tensor filled with the scalar value `1`, with the shape defined\n",
      "    by the variable argument :attr:`size`.\n",
      "    \n",
      "    Args:\n",
      "        size (int...): a sequence of integers defining the shape of the output tensor.\n",
      "            Can be a variable number of arguments or a collection like a list or tuple.\n",
      "    \n",
      "    Keyword arguments:\n",
      "        out (Tensor, optional): the output tensor.\n",
      "        dtype (:class:`torch.dtype`, optional): the desired data type of returned tensor.\n",
      "            Default: if ``None``, uses a global default (see :func:`torch.set_default_tensor_type`).\n",
      "        layout (:class:`torch.layout`, optional): the desired layout of returned Tensor.\n",
      "            Default: ``torch.strided``.\n",
      "        device (:class:`torch.device`, optional): the desired device of returned tensor.\n",
      "            Default: if ``None``, uses the current device for the default tensor type\n",
      "            (see :func:`torch.set_default_tensor_type`). :attr:`device` will be the CPU\n",
      "            for CPU tensor types and the current CUDA device for CUDA tensor types.\n",
      "        requires_grad (bool, optional): If autograd should record operations on the\n",
      "            returned tensor. Default: ``False``.\n",
      "    \n",
      "    Example::\n",
      "    \n",
      "        >>> torch.ones(2, 3)\n",
      "        tensor([[ 1.,  1.,  1.],\n",
      "                [ 1.,  1.,  1.]])\n",
      "    \n",
      "        >>> torch.ones(5)\n",
      "        tensor([ 1.,  1.,  1.,  1.,  1.])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(torch.ones)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8f4283d",
   "metadata": {
    "origin_pos": 14
   },
   "source": [
    "从文档中，我们可以看到`ones`函数创建一个具有指定形状的新张量，并将所有元素值设置为1。\n",
    "下面来[**运行一个快速测试**]来确认这一解释：\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a66953e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-12-07T16:28:24.993229Z",
     "iopub.status.busy": "2022-12-07T16:28:24.992792Z",
     "iopub.status.idle": "2022-12-07T16:28:25.006286Z",
     "shell.execute_reply": "2022-12-07T16:28:25.005519Z"
    },
    "origin_pos": 16,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 1., 1., 1.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.ones(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9056fb3d",
   "metadata": {
    "origin_pos": 19
   },
   "source": [
    "在Jupyter记事本中，我们可以使用`?`指令在另一个浏览器窗口中显示文档。\n",
    "例如，`list?`指令将创建与`help(list)`指令几乎相同的内容，并在新的浏览器窗口中显示它。\n",
    "此外，如果我们使用两个问号，如`list??`，将显示实现该函数的Python代码。\n",
    "\n",
    "## 小结\n",
    "\n",
    "* 官方文档提供了本书之外的大量描述和示例。\n",
    "* 可以通过调用`dir`和`help`函数或在Jupyter记事本中使用`?`和`??`查看API的用法文档。\n",
    "\n",
    "## 练习\n",
    "\n",
    "1. 在深度学习框架中查找任何函数或类的文档。请尝试在这个框架的官方网站上找到文档。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d3bed2c",
   "metadata": {
    "origin_pos": 21,
    "tab": [
     "pytorch"
    ]
   },
   "source": [
    "[Discussions](https://discuss.d2l.ai/t/1765)\n"
   ]
  }
 ],
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